Modeling online adaptive navigation in virtual environments based on PID control

It is well known that locomotion-dominated navigation tasks may highly provoke cybersickness effects. Past research has proposed numerous approaches to tackle this issue based on offline considerations. In this work, a novel approach to mitigate cybersickness is presented based on online adaptative navigation. Considering the Proportional-Integral-Derivative (PID) control method, we proposed a mathematical model for online adaptive navigation parameterized with several parameters, taking as input the users' electro-dermal activity (EDA), an efficient indicator to measure the cybersickness level, and providing as output adapted navigation accelerations. Therefore, minimizing the cybersickness level is regarded as an argument optimization problem: find the PID model parameters which can reduce the severity of cybersickness. User studies were organized to collect non-adapted navigation accelerations and the corresponding EDA signals. A deep neural network was then formulated to learn the correlation between EDA and navigation accelerations. The hyperparameters of the network were obtained through the Optuna open-source framework. To validate the performance of the optimized online adaptive navigation developed through the PID control, we performed an analysis in a simulated user study based on the pre-trained deep neural network. Results indicate a significant reduction of cybersickness in terms of EDA signal analysis and motion sickness dose value. This is a pioneering work which presented a systematic strategy for adaptive navigation settings from a theoretical point

Machine learning methods for the study of cybersickness: a systematic review

This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness

Prediction of cybersickness in virtual environments using topological data analysis and machine learning

Recent significant progress in Virtual Reality (VR) applications and environments raised several challenges. They proved to have side effects on specific users, thus reducing the usability of the VR technology in some critical domains, such as flight and car simulators. One of the common side effects is cybersickness. Some significant commonly reported symptoms are nausea, oculomotor discomfort, and disorientation. To mitigate these symptoms and consequently improve the usability of VR systems, it is necessary to predict the incidence of cybersickness. This paper proposes a machine learning approach to VR’s cybersickness prediction based on physiological and subjective data. We investigated combinations of topological data analysis with a range of classifier algorithms and assessed classification performance. The highest performance of Topological Data Analysis (TDA) based methods was achieved in combination with SVMs with Gaussian RBF kernel, indicating that Gaussian RBF kernels provide embeddings of physiological time series data into spaces that are rich enough to capture the essential geometric features of this type of data. Comparing several combinations with feature descriptors for physiological time series, the performance of the TDA + SVM combination is in the top group, statistically being on par or outperforming more complex and less interpretable methods. Our results show that heart rate does not seem to correlate with cybersickness

VR Sickness Prediction for Navigation in Immersive Virtual Environments using a Deep Long Short Term Memory Model

International audienceThis paper proposes a new objective metric of visually induced motion sickness (VIMS) in the context of navigation in virtual environments (VEs). Similar to motion sickness in physical environments, VIMS can induce many physiological symptoms such as general discomfort, nausea, disorientation, vomiting, dizziness and fatigue. To improve user satisfaction with VR applications, it is of great significance to develop objective metrics for VIMS that can analyze and estimate the level of VR sickness when a user is exposed to VEs. One of the well-known objective metrics is the postural instability. In this paper, we trained a LSTM model for each participant using a normal-state postural signal captured before the exposure, and if the postural sway signal from post-exposure was sufficiently different from the pre-exposure signal, the model would fail at encoding and decoding the signal properly; the jump in the reconstruction error was called loss and was proposed as the proposed objective measure of simulator sickness. The effectiveness of the proposed metric was analyzed and compared with subjective assessment methods based on the simulator sickness questionnaire (SSQ) in a VR environment, achieving a Pearson correlation coefficient of .89. Finally, we showed that the proposed method had the potential to be deployed within a closed-loop system and get real-time performance to predict VR sickness, opening new insights to develop user-centered and customized VR applications based on physiological feedback

Forecasting the Onset of Cybersickness using Physiological Data

指導教員：Machael Vallanc

The effect of visual detail on cybersickness:predicting symptom severity using spatial velocity

Abstract. In this work, we examine the effect of visual realism on the severity of cybersickness symptoms experienced by users of virtual environments. We also seek to validate a metric called spatial velocity as a predictor of cybersickness. The proposed metric combines the visual complexity of a virtual scene with the amount of movement within the scene. To achieve this, we prepared two virtual scenes depicting the same environment with a variable level of detail. We recruited volunteers who were exposed to both scenes in two separate sessions. We obtained the sickness ratings after both sessions and saved the data required for spatial velocity calculations. After comparing the sickness ratings between the two scenes, we found no evidence of the visual realism playing any significant role in the generation of cybersickness symptoms. The spatial velocity also proved inadequate in characterizing the difference in visual complexity and correlated poorly with all the observed sickness scores.Visuaalisen yksityiskohtaisuuden vaikutus VR-pahoinvointiin : oireiden vakavuuden ennustaminen käyttäen SV-metriikkaa. Tiivistelmä. Tässä työssä tutkimme sitä, millainen vaikutus virtuaalisten ympäristöjen graafisella yksityiskohtaisuudella on VR-pahoinvointiin. Pyrimme myös validoimaan "spatial velocity" -nimisen mittasuureen kyvyn ennustaa VR-pahoinvoinnin oireiden vakavuutta. Kyseisen mittasuureen etuna on, että se yhdistää visuaalisen kompleksisuuden ja ympäristössä koetun liikkeen yhdeksi suureeksi. Tutkimusta varten valmistimme kaksi virtuaaliympäristöä, joissa mallinnettiin Oulun yliopiston kampusaluetta. Toinen ympäristö pyrki mahdollisimman realistiseen esitystapaan, kun taas toisessa yksityiskohtien määrä minimoitiin. Koetta varten värväsimme 18 vapaaehtoista. Vapaaehtoiset altistettiin kummallekin ympäristölle kahdessa noin kymmenen minuutin mittaisessa kokeessa. Vapaaehtoisten kokeman VR-pahoinvoinnin vakavuutta arvioitiin kunkin kokeen jälkeen täytetyillä kyselylomakkeilla. Kokeiden aikana tallensimme myös SV laskentaan tarvittavat tiedot. Verrattuamme koeolosuhteiden tuloksia, emme löytäneet todisteita siitä, että ympäristön graafisten yksityiskohtien määrällä olisi merkittävää vaikutusta koettuun pahoinvointiin. Käytetty SV metriikka ei myöskään kyennyt erottelemaan ympäristöjä oletetulla tavalla, eivätkä lasketut arvot korreloineet merkittävästi minkään mitatun pahoinvointisuureen kanssa